A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Looking for a way to download files from pan.baidu.com (Baidu Netdisk) for free? Here’s a simple step-by-step tutorial using a third-party website that generates direct download links.

LUKS2 encrypted BTRFS system partition with Limine/Snapper integration and hybernate to swapfile

• Table of Contents
• Introduction
• Preparation
• Ensure proper network connectivity

This document describes the working setup for running the native Antigravity CLI (agy) on Android Termux.

The main goal is to run the official Linux ARM64 Antigravity binary directly from Termux without Cloud Shell, without a full VM, and without replacing Android. The setup uses a small binary patch plus a wrapper that adapts the Linux/glibc binary to Termux's Android environment.

The method is designed to be repeatable for future Antigravity releases. The patch script scans instruction patterns instead of relying on fixed offsets. If Antigravity changes its allocator code generation in the future, the script will print warning counts instead of silently claiming success.

Antigravity's Linux ARM64 binary can fail on Termux for multiple independent reasons.